At present, with the development of the computer technology and the gradual popularization of the Internet, more and more people acquire a variety of information through the Internet. And correspondingly, the amount of information on the Internet has become more abundant with the development of the computer technology and the popularity of the Internet.
In recent years, with the rapid development of mobile Internet, people have gradually been accustomed to acquiring information content through the information client on the mobile terminal. In this way, the time that a user acquires information through the network becomes more fragmented. In this context, how to accurately provide users with valuable information that users are interested in becomes more important. In particular, it is an urgent problem to provide new users with valuable and interesting information.
In the traditional technologies, the cold start problem of the recommendation system is a major challenge in the application of the products such as information clients. Herein the cold start problem of the recommendation system refers to the fact that the new user system lacks sufficient data for capturing effective recommended content that users are interested in. There is a widely used method in a number of solutions to the problem, that is, encouraging users to login the recommendation system with a Social Network Service (SNS) account, for example: login with a social account such as microblog, Tencent QQ and Renren. The recommendation system can use the information of a user in a social platform (for example, followed relationship, a friend relationship, an interest label, published content, etc.) to initialize the interest model of the user to make effective recommendation.
On the one hand, there are still a lot of difficulties in using public data from a social platform for content recommendation (public data, such as video, articles, pictures, music, games, software, friends, etc.) in the practical application. For example, the published content of the social platform is often shorter and messy, the label content of a user is often unconventional (such as: aliens will die without sleeping late, intensive phobia late patients, etc.), it is more difficult to understand by a machine learning algorithm and it is limited in helping improve the recommended service. For users who are not active on the SNS and have weak social relationships, public data on their SNS platforms is more limited in improving the recommendation effect. On the other hand, a mature content recommended service provider with a larger number of users often has accumulated a lot of user behavior information in the long-term operation process, such as: on-demand video and articles read or commented by the user. If this part of data may be effectively integrated and used with the public data of the SNS, it is possible to greatly improve the recommendation effect of the user. However, the existing technology basically focuses on the use of the public data provided by the SNS platform to mine a user interest model and recommend. It is difficult to achieve this method, and the accuracy is low.
There is no effective solution for the problem in the traditional art that targeted information cannot be provided because a newly registered user has no historical browsing record.